Blue-printing interference for LTE access in unlicensed spectrum

ABSTRACT

A system, method, and computer program product are provided for blue-printing interference for mobile access in an unlicensed spectrum of a synchronous scheduled cellular access system. The system includes a cellular base station having a processor. The processor constructs and executes an intelligent measurement schedule of clients for uplink transmissions to obtain access measurements for the uplink transmissions. The intelligent measurement schedule is constructed for scalable access measurement overhead. The access measurements indicate interference dependencies between the clients. The processor estimates an interference topology and statistics of the interference topology, from the access measurements to form an interference blueprint. The processor adjusts the intelligent measurement schedule to overschedule the clients for the uplink transmissions to reduce spectrum utilization loss while minimizing client transmission collisions, based on the interference blueprint. The processor initiates the uplink transmissions for the clients in accordance with the adjusted intelligent measurement schedule.

RELATED APPLICATION INFORMATION

This application claims priority to provisional application Ser. No.62/471,549 filed on Mar. 15, 2017, and provisional application Ser. No.62/615,477 filed on Jan. 10, 2018, all incorporated herein by reference.

BACKGROUND Technical Field

The present invention relates to cellular communications, and moreparticularly to blue-printing interference for LTE access in unlicensedspectrum.

Description of the Related Art

Wireless interference is a significant source of performance degradationin wireless networks. The problem is especially acute in unlicensedspectrum bands (e.g., Industrial, Scientific, and Medical (ISM) andCitizens Broadband Radio Service (CBRS) bands), where multiple devicesbelonging to different service providers all operate in the samespectrum simultaneously, thereby leading to significant interference.

While this problem exists in WIFI, which operates in the ISM band, WIFIis a technology having incorporated asynchronous channel sensingmechanisms as part of its access protocol to sense and avoid suchunknown interference. However, with the scarcity of spectrum as wirelessnetworks migrate to 5G, cellular networks are starting to aggregateunlicensed spectrum bands along with their unlicensed bands to delivercapacity. However, the synchronous access mechanisms developed forcellular access protocols in licensed spectrum are no longer effectivewhen operating in unlicensed spectrum that is governed by asynchronous,unknown interference. Consequently, cellular networks operating inunlicensed spectrum suffer significant performance degradation. Ifcellular networks have the necessary means to identify and estimate suchunknown interference in unlicensed spectrum, this could significantlyimprove their performance in unlicensed spectrum. Thus, there is a needfor a way for cellular networks to be able to identify and estimate suchunknown interference in unlicensed spectrum.

SUMMARY

According to an aspect of the present invention, a system is providedfor blue-printing interference for mobile access in an unlicensedspectrum of a synchronous scheduled cellular access system. The systemincludes a cellular base station having a processor. The processor isconfigured to construct and execute an intelligent measurement scheduleof clients for uplink transmissions to obtain access measurements forthe uplink transmissions. The intelligent measurement schedule isconstructed for scalable access measurement overhead. The accessmeasurements indicate interference dependencies between the clients. Theprocessor is further configured to estimate an interference topology andstatistics of the interference topology, from the access measurements toform an interference blueprint. The processor is also configured toadjust the intelligent measurement schedule to overschedule the clientsfor the uplink transmissions to reduce spectrum utilization loss whileminimizing client transmission collisions, based on the interferenceblueprint. The processor is additionally configured to initiate theuplink transmissions for the clients in accordance with the adjustedintelligent measurement schedule.

According to another aspect of the present invention, acomputer-implemented method is provided for blue-printing interferencefor mobile access in an unlicensed spectrum of a synchronous scheduledcellular access system. The method includes constructing and executing,by a processor of a cellular base station, an intelligent measurementschedule of clients for uplink transmissions to obtain accessmeasurements for the uplink transmissions. The intelligent measurementschedule is constructed for scalable access measurement overhead. Theaccess measurements indicating interference dependencies between theclients. The method further includes estimating, by the processor, aninterference topology and statistics of the interference topology, fromthe access measurements to form an interference blueprint. The methodalso includes adjusting, by the processor, the intelligent measurementschedule to overschedule the clients for the uplink transmissions toreduce spectrum utilization loss while minimizing client transmissioncollisions, based on the interference blueprint. The method additionallyincludes initiating, by the processor, the uplink transmissions for theclients in accordance with the adjusted intelligent measurementschedule.

According to yet another aspect of the present invention, a computerprogram product is provided for blue-printing interference for mobileaccess in an unlicensed spectrum of a synchronous scheduled cellularaccess system. The computer program product includes a non-transitorycomputer readable storage medium having program instructions embodiedtherewith. The program instructions are executable by a computer of acellular base station to cause the computer to perform a method. Themethod includes constructing and executing, by a processor of thecomputer, an intelligent measurement schedule of clients for uplinktransmissions to obtain access measurements for the uplinktransmissions. The intelligent measurement schedule is constructed forscalable access measurement overhead. The access measurements indicateinterference dependencies between the clients. The method furtherincludes estimating, by the processor, an interference topology andstatistics of the interference topology, from the access measurements toform an interference blueprint. The method also includes adjusting, bythe processor, the intelligent measurement schedule to overschedule theclients for the uplink transmissions to reduce spectrum utilization losswhile minimizing client transmission collisions, based on theinterference blueprint. The method additionally includes initiating, bythe processor, the uplink transmissions for the clients in accordancewith the adjusted intelligent measurement schedule.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a block diagram showing an exemplary environment to which thepresent invention can be applied, in accordance with an embodiment ofthe present invention;

FIG. 2 is a high-level block diagram showing an exemplary system forblue-printing interference for LTE access in unlicensed spectrum, inaccordance with an embodiment of the present invention;

FIG. 3 is a flow diagram showing an exemplary method for blue-printinginterference for LTE access in unlicensed spectrum, in accordance withan embodiment of the present invention;

FIG. 4 is a flow diagram further showing a block of the method of FIG.3, in accordance with an embodiment of the present invention;

FIG. 5 is a flow diagram further showing another block of the method ofFIG. 3, in accordance with an embodiment of the present invention;

FIG. 6 is a flow diagram further showing a block of the method of FIG.5, in accordance with an embodiment of the present invention;

FIG. 7 is a block diagram showing an exemplary processing system towhich the present principles may be applied, according to an embodimentof the present principles;

FIG. 8 is a block diagram showing an exemplary topology to which thepresent invention can be applied, in accordance with an embodiment ofthe present invention;

FIG. 9 is a block diagram showing an exemplary topology inferenceobjective, in accordance with an embodiment of the present invention;

FIG. 10 is a block diagram showing an exemplary a graphical constraintsatisfiability problem, in accordance with an embodiment of the presentinvention; and

FIG. 11 is a block diagram showing an exemplary topology conditioning,in accordance with an embodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The present invention is directed to blue-printing interference for LTEaccess in unlicensed spectrum.

The present invention solves the aforementioned interference problem forLTE access in unlicensed spectrum by, for example, assisting LTE basestations to blue-print (estimate) the complete interference topology(i.e., the set of interfering sources as well as their operationstatistics) that impacts the set of clients that is serves with minimaloverhead. With such information, several useful applications can beenabled including, but not limited to the following: (i) better accessprotocols that intelligently determine how to schedule clients tomaximize spectrum utilization and throughput efficiency; (ii) assistancelocalizing the clients relative to each other, or localize theinterference sources if client locations are known; (iii) improvedchannel selection decisions based on observed interference in variousunlicensed spectrum channels; (iv) and so forth.

FIG. 1 is a block diagram showing an exemplary environment 100 to whichthe present invention can be applied, in accordance with an embodimentof the present invention.

The environment 100 relates to an LTE uplink in unlicensed spectrum thatis affected by hidden terminals (H1-H3) as described in further detailherein below.

The environment 100 includes an LTE base station 110 and a set ofclients 120A-N that are served by the LTE base station 110. The set ofclients 120A-N can represent a set of User Equipment (UE). The UE can beany of, but not limited to, cellular phones as well as tablets, laptops,and any other computing devices capable of cellular communication. Theenvironment 100 further includes hidden terminals H1-H3, respectivelycorresponding to a WIFI node 130A, a WIFI node 130B, and another basestation 140. The LTE base station 110 can include or be coupled to aprocessing system 180 for controlling the LTE base station 110.

One of the key differences between LTE and WIFI is the synchronous andscheduled nature of LTE transmissions compared to the asynchronous WIFItransmissions. Synchronous transmissions in LTE contribute to increasedcapacity through multi-user diversity (OF-DMA) and spatial multiplexing(multi-user MIMO) gains, especially on the uplink, where it is otherwisechallenging to synchronize clients (e.g., clients 120A-N). However,these very same features make it particularly challenging for realizinggains in unlicensed spectrum, where the impact of asynchronousinterference (through hidden terminals from WIFI (e.g., WIFI nodes 130Aand 130B) or other LTE nodes (e.g., base station 140)) on concurrenttransmissions is significantly amplified. This reveals a fundamentalconflict between the synchronous multi-user transmissions in LTE andasynchronous access in unlicensed spectrum.

FIG. 2 is a high-level block diagram showing an exemplary system 200 forblue-printing interference for LTE access in unlicensed spectrum, inaccordance with an embodiment of the present invention.

The system 200 includes a scalable measurement scheduler 210, aninterference topology inference generator 220, a higher order accessdistributions generator 230, a measurement schedule optimizer 240, ameasurement from schedule collector 250, a speculative schedule computer260, and a speculative schedule executor 270.

The present invention orchestrates its various design components toexecute its speculative scheduler at eNBs (LTE base stations) as shownin FIG. 2. The present invention operates the uplink eNB schedule in twophases repeatedly: a measurement schedule phase (involving the eNB andUEs) for t_(max) sub-frames; and a speculative schedule phase (involvingthe eNB and UEs) for L sub-frames (L>>t_(max)). In the measurementphase, clients are scheduled with the objective of obtaining the desiredclient access distributions (p(i), p(i, j)) with minimal overhead. Inthe speculative scheduling phase, the present invention blueprints thesource interference topology from the measured distributions and uses itto determine the higher-order joint client access distributions that areneeded for speculatively scheduling, especially MU-MIMO transmissionsfor higher utilization and efficiency.

L is chosen to track dynamics in the topology (both clients andinterference), which happens at the granularity of tens of seconds tominutes. Hence, L is in the order of tens of thousands of sub-frames,while t_(max) is in the order of few hundred sub-frames. For example,for a 20 (N) client cell with a 50 (T) measurement sample requirementper client-pair, and maximum of 8 (K) distinct clients per sub-frameschedule, t_(max)≈340 sub-frames. Further, note that, other than thefirst time that the eNB is operated, the measurement phase is run forless than t_(max) sub-frames, as the outcome of the schedule during thespeculative phase will implicitly contribute to measurements as well.Hence, the measurement phase can be significantly reduced by removingclient pairs for which, sufficient data is already available and forthose, whose clients are inferred to have independent accessdistributions (e.g., located far away from each other). Thus, themeasurement phase constitutes a very small part of the whole UL schedulethat is predominantly used to maximize UL utilization throughspeculative scheduling.

FIG. 3 is a flow diagram showing an exemplary method 300 forblue-printing interference for LTE access in unlicensed spectrum, inaccordance with an embodiment of the present invention.

At block 310, construct and execute an intelligent measurement schedule(hereinafter “intelligent schedule” in short) of clients for uplinktransmissions to obtain access measurements (aka observed measurements)for the uplink transmissions. The intelligent schedule is constructedfor scalable access measurement overhead. In an embodiment, theintelligent schedule can relate to the LTE uplink in unlicensedspectrum.

At block 320, estimate an interference topology and statistics of theinference topology, from the access measurements to form an interferenceblueprint.

At block 330, adjust the intelligent schedule to overschedule theclients for the uplink transmissions to reduce spectrum utilization losswhile minimizing client transmission collisions, based on theinterference blueprint. The overscheduling advantageously factors inexpected spectrum utilization loss in relation to the interferenceblueprint in order to ultimately reduce the spectrum utilization losswhile minimizing client transmission collisions.

At block 340, initiate the uplink transmissions for the clients inaccordance with the adjusted intelligent schedule.

FIG. 4 is a flow diagram further showing block 310 of method 300 of FIG.3, in accordance with an embodiment of the present invention. Block 310in FIG. 3 is represented by blocks 410-430 in FIG. 4.

At block 410, it is determined whether or not “T” observations(measurements) have been jointly scheduled for every pair of clients. Ifso, then the method proceeds to block 420. Otherwise, the methodproceeds to block 430.

At block 420, execute a computed schedule, collect measurements, obtainindividual p(i) and pair-wise p(i,j) joint client access probabilities(statistics).

At block 430, add a Tx slot (frame) to the schedule. Start with an emptyschedule for the slot and add “k” clients, one at a time, such that theadded client yields the maximum value to the required measurementstatistics based on the existing schedule that has been computed thusfar.

FIG. 5 is a flow diagram further showing block 320 of method 300 of FIG.3, in accordance with an embodiment of the present invention. Block 320in FIG. 3 is represented by blocks 510-520 m in FIG. 5.

At block 510, represent the problem as a constraint satisfiabilityproblem on a graph through a transformation. The graph has three sets ofvertices as follows. A first set of vertices represents the clients andhas a value that is a function of their individual access probabilities(−log p(i)). A second set of vertices represents the set of unknownnumber of interfering sources (hidden terminals) and their respectiveunknown access probabilities (q(k)). A third set of vertices representspairs of clients with a value that is a function of their joint accessprobabilities (−log p(i)*p(j)/p(i,j))). Edges exist between the firstand second set of vertices (z_(ik)), as well as the third and secondsets of vertices (z_(jk)) and represent the unknown interference(constraints) caused by hidden terminals on the clients represented bythe vertices. The objective of solving the constraint satisfiabilityproblem is to determine (infer) the number of hidden terminals and theiraccess probabilities as well as the edges (interference dependencies)between hidden terminals and the two set of vertices that wereresponsible for the observed client access probabilities.

At block 520, employ Bayesian learning (e.g., a Monte Carlo Markovchain) or deterministic approaches to solve the constraintsatisfiability problem. In an embodiment, block 520 can involve aniterative approach.

FIG. 6 is a flow diagram further showing block 520 of method 500 of FIG.5, in accordance with an embodiment of the present invention.

At block 610, initialize the starting inference topology randomly.

At block 620, at every iteration, adopt a gradient approach by, forexample, picking the constraint that shows the maximum violation, andthen picking a hidden terminal along with this topology (interferenceedge) adaptation that will resolve this (i.e., the maximum) violation,while minimizing the violation caused to other constraints in theprocess. Terminate when all constraints are satisfied or a maximumnumber of iterations have been reached.

At block 630, among the various starting point initializations, selectthe one that yields the topology with zero or minimal violation.

FIG. 7 is a block diagram showing an exemplary processing system 700 towhich the present principles may be applied, according to an embodimentof the present principles.

The processing system 700 includes at least one processor (CPU) 704operatively coupled to other components via a system bus 702. A cache706, a Read Only Memory (ROM) 708, a Random Access Memory (RAM) 710, aninput/output (I/O) adapter 720, a sound adapter 730, a network adapter740, a user interface adapter 750, and a display adapter 760, areoperatively coupled to the system bus 702.

A first storage device 722 and a second storage device 724 areoperatively coupled to system bus 702 by the I/O adapter 720. Thestorage devices 722 and 724 can be any of a disk storage device (e.g., amagnetic or optical disk storage device), a solid state magnetic device,and so forth. The storage devices 722 and 724 can be the same type ofstorage device or different types of storage devices.

A speaker 732 is operatively coupled to system bus 702 by the soundadapter 730. A transceiver 742 is operatively coupled to system bus 702by network adapter 740. A display device 762 is operatively coupled tosystem bus 702 by display adapter 760.

A first user input device 752, a second user input device 754, and athird user input device 756 are operatively coupled to system bus 702 byuser interface adapter 750. The user input devices 752, 754, and 756 canbe any of a keyboard, a mouse, a keypad, an image capture device, amotion sensing device, a microphone, a device incorporating thefunctionality of at least two of the preceding devices, and so forth. Ofcourse, other types of input devices can also be used, while maintainingthe spirit of the present principles. The user input devices 752, 754,and 756 can be the same type of user input device or different types ofuser input devices. The user input devices 752, 754, and 756 are used toinput and output information to and from system 700.

Of course, the processing system 700 may also include other elements(not shown), as readily contemplated by one of skill in the art, as wellas omit certain elements. For example, various other input devicesand/or output devices can be included in processing system 700,depending upon the particular implementation of the same, as readilyunderstood by one of ordinary skill in the art. For example, varioustypes of wireless and/or wired input and/or output devices can be used.Moreover, additional processors, controllers, memories, and so forth, invarious configurations can also be utilized as readily appreciated byone of ordinary skill in the art. These and other variations of theprocessing system 700 are readily contemplated by one of ordinary skillin the art given the teachings of the present principles providedherein.

Moreover, it is to be appreciated that system 100 described above withrespect to FIG. 1 is a system for implementing respective embodiments ofthe present principles. Part or all of processing system 700 may beimplemented in one or more of the elements of system 100. For example,processing system 700 can be included the base station of FIG. 1, inaccordance with an embodiment of the present invention.

Further, it is to be appreciated that processing system 700 may performat least part of the methods described herein including, for example, atleast part of method 300 of FIG. 3 and/or at least part of method 400 ofFIG. 4 and/or at least part of method 500 of FIG. 5 and/or at least partof method 600 of FIG. 6. Moreover, system 200 may perform at least partof the methods described herein including, for example, at least part ofmethod 300 of FIG. 3 and/or at least part of method 400 of FIG. 4 and/orat least part of method 500 of FIG. 5 and/or at least part of method 600of FIG. 6.

A description will now be given regarding various aspects of the presentinvention.

LTE Overview: LTE is a synchronous, scheduled access system designed foroperation in the licensed spectrum. The eNB is responsible forscheduling both the downlink (DL) and uplink (UL) clients in itssub-frames (1 ms long), which consists of two-dimensional resourceelements spanning both time (symbols) and frequency (sub-carriers),called resource blocks (RBs). LTE employs OFDMA (orthogonal frequencydivision multiple access), whereby multiple clients are scheduled ineach sub frame across RBs and, in the case of multi-user MIMO, multipleclients are scheduled on each RB. The schedule for both downlink (DL)and uplink (UL) transmissions is conveyed to the clients through thecontrol part of the DL sub-frames.

LTE in Unlicensed Spectrum: Unlike traditional LTE that operates in analways-on mode in licensed spectrum, operating in the unlicensedspectrum requires LTE to adopt asynchronous access principles of energysensing (clear-channel assessment, CCA) and back-off for coexistencewith the incumbents.

A conflict arises as follows in the form of a pronounced interferenceimpact on UL Access: Asynchronous access in WIFI is largely atransmitter-driven procedure (in the absence of Request to Send/Clear toSend (RTS/CTS)), where the sender (access point (AP) in DL and client inUL) senses (cyclic cellular automaton (CCA)) the channel for idle statebefore starting its transmission. LTE adopts a similar approach in itsDL, with one difference being that once the eNB gains access, it willtransmit to multiple users simultaneously using OFDMA (compared tosingle-user OFDM in WIFI). This leads to a more frequent impact frominterference (compared to WIFI) such as an increased probability of someclient in each sub-frame being prone to interference (collision) fromhidden terminals. However, the magnitude of the impact is restricted toa smaller chunk of the spectrum compared to the entire bandwidth inWIFI. Hence, the over all impact of interference on DL access is largelysimilar in both WIFI and LTE.

However, the problem is significantly different on the UL. Again, theeNB senses the channel (compared to clients themselves in WIFI) andschedules the synchronous access of multiple clients on the UL in LTE.This makes it possible to realize the gains from OFDMA and MU-MIMO,which are otherwise not possible on the UL (e.g., WIFI). However, sincethe instantaneous channel (interference) state of the clients cannot beknown a priori in unlicensed spectrum, a scheduled LTE client that isinhibited by an interfering transmission (hidden to the eNB) will not beable to utilize its allocated UL grant. This leads to anunderutilization of spectral resources, which is a problem that is notencountered in WIFI and exacerbated by multi-user access in LTE.

Impact Scales with Concurrency of Transmissions: To understand themagnitude of this problem, we collect access traces from an LTEsingle-cell test-bed, where the UL access of clients in the cell areimpacted by WIFI hidden terminals as shown in FIG. 1. The loss inutilization scales with the number of hidden terminals as well asclients scheduled in each sub-frame, as both factors increase theprobability of scheduled grants going unused in a sub-frame. With bothOFDMA and MU-MIMO relying on an increased number of scheduled clients,the loss in utilization can be significantly high. This reveals afundamental conflict between leveraging the increased gains (diversityfrom OFDMA and multiplexing from MU-MIMO) from LTE's synchronous,concurrent transmissions on the UL, and coexistence (asynchronousinterference) in unlicensed spectrum.

A description will now be given regarding speculative scheduling inBETL, in accordance with an embodiment of the present invention.

LTE schedulers employ orthogonal frequency division multiple access(OFDMA) to leverage multi-user diversity. The spectrum (e.g., 20 MHzchannel) is partitioned into resource blocks (groups of OFDMsub-carriers) and a user (users in case of MU-MIMO) with a higher rateon a RB is assigned to it, while accounting for fairness across clients.Proportional fair (PF) scheduling is the most popular scheduling modeladopted in eNBs today as it strikes a good balance between throughputefficiency and fairness, allowing for clients with better channels toachieve a proportionally higher throughput.

The optimal scheduling policy can be obtained through a utilityoptimization framework that maximizes the aggregate utility of all theclients (Σ_(i)U_(i)). For PF scheduling, the utility function is thelogarithm of the client's average throughput U_(i)=log(R_(i)). Being aconvex optimization problem, picking a schedule that maximizes thegradient of the utility (i.e., marginal utility,

$\left. {\frac{{dU}_{i}}{dt} = \frac{r_{i}(t)}{R_{i}\left( {t - 1} \right)}} \right)$at each sub-frame t, achieves proportional fairness over a longer timeperiod. The scheduling problem for each sub-frame with B sub frames andN clients now reduces to the following:

${{S^{*}(t)}\begin{matrix} = \\{SISO}\end{matrix}\arg\;{\max\limits_{x \in S}\left\{ {\sum\limits_{b = 1}^{B}{\sum\limits_{i = 1}^{N}\frac{x_{i,b}{r_{i,b}(t)}}{R_{i}\left( {t - 1} \right)}}} \right\}}},{{s.t.\mspace{14mu}{\sum\limits_{i = 1}^{N}x_{i,b}}} \leq 1},{\forall b}$${\begin{matrix} = \\{{MU}\text{-}{MIMO}}\end{matrix}\arg\;{\max\limits_{y \in S}\left\{ {\sum\limits_{b = 1}^{B}{\sum\limits_{i = 1}^{N}\frac{y_{i,b}{r_{i,b,g}(t)}}{R_{i}\left( {t - 1} \right)}}} \right\}}},{{s.t.\mspace{14mu}{\sum\limits_{i = 1}^{N}y_{i,b}}} \leq M},{\forall b}$where r_(i,b)(t) and r_(i,b,g) (t) are the instantaneous rates of clienti on RB b in SISO and MU-MIMO (depends on the group of clients gselected as well as their channels) respectively, while x and y arebinary variables capturing the schedule. The above scheduling problemcan be decoupled into multiple (individual) RB-level schedulingproblems, S_(b)*(t). After each schedule, the average throughput of aclient i gets updated as follows:

${{R_{i}(t)}\begin{matrix} = \\{SISO}\end{matrix}\frac{1}{\alpha}{\sum\limits_{b = 1}^{B}{x_{i,b}^{*}{r_{i,b}(t)}}}} + {\left( {1 - \frac{1}{\alpha}} \right){R_{i}\left( {t - 1} \right)}}$${\begin{matrix} = \\{{MU}\text{-}{MIMO}}\end{matrix}\frac{1}{\alpha}{\sum\limits_{b = 1}^{B}{y_{i,b}^{*}{r_{i,b,g}(t)}}}} + {\left( {1 - \frac{1}{\alpha}} \right){R_{i}\left( {t - 1} \right)}}$where α is an exponential weighting constant. We will now focus on asub-frame and hence drop the subscript of t for an easier exposition.

A description will now be given regarding the scheduler leveraginginterference diversity, in accordance with an embodiment of the presentinvention.

Since the clients are scheduled by the eNB on the UL, interferers (WIFIor other LTE nodes) to the clients that are hidden from the eNB willprevent the clients from utilizing the allocated resource grants. Ifp(i) is the probability that client i is able to utilize its allocatedgrant, then the expected value of the schedule S* (for SISO) reduces tothe following:

$\begin{matrix}{{E\left( S^{*} \right)} = {\sum\limits_{b = 1}^{B}{\sum_{i \in S_{b}^{*}}\frac{{p(i)} \cdot r_{i,b}}{R_{i}}}}} & (1)\end{matrix}$

Depending on the impact of hidden terminals (reduced p(i)), existingschedulers, albeit efficient for licensed spectrum, can lead tosignificant underutilization in unlicensed spectrum.

BETL transforms the very challenge posed by scheduled, multi-user LTEtransmissions into an opportunity as follows. Different clients in thesame cell could be interfered by different hidden terminals (e.g.,clients 1 and 3 in FIG. 1) and hence may not be silenced at the sametime. BETL leverages this interference diversity across clients, coupledwith LTE's ability to simultaneously schedule multiple users in an ULsub-frame, to overschedule multiple users (>M) on the same UL resourceblock to increase utilization. However, executing this intelligently byidentifying which clients need to be overscheduled on the same RB isparamount, as multiple client transmissions (>1 for SISO and >M forMU-MIMO) on the same RB will lead to collisions and, hence, a much worseperformance than the underutilized schedule.

BETL makes its decisions based on the expected utility of a schedulethat accounts for the joint (dependent) stochastic access patterns ofthe clients. For a given RB b and an existing set of clients (G_(b))scheduled on it, BETL selects and adds another client l⁺ that providesthe maximum incremental utility to the current schedule on that RB asfollows:

$\begin{matrix}{{\ell^{*} = {\arg\mspace{11mu}{\max\limits_{\ell \notin G_{b}}\left\{ {{E\left( G_{b}^{\prime} \right)} - {E\left( G_{b} \right)}} \right\}}}};\left. {{where}\mspace{14mu} G_{b}^{\prime}}\leftarrow{G_{b}\bigcup\ell} \right.} & (2)\end{matrix}$

In its most generic form, the expected utility of a schedule on a RBdepends on the total number of its scheduled clients, who can use thegrant, being less than or equal to the total number of antennas (M) atthe eNB, their joint access distribution, and the utility of thosespecific clients in the group as follows:

$\begin{matrix}{{E\left( G_{b}^{\prime} \right)} = {\sum_{{{{g:{g \subseteq G_{b}^{\prime}}}\&}{g}}<=M}\left( {\left( {g,\overset{\_}{G_{b}^{\prime}/g}} \right){\sum_{i \in g}\frac{r_{i,b,g}}{R_{i}}}} \right)}} & (3)\end{matrix}$where,

(g, G′_(b)/g)³ represents the joint access distribution of the group,that is, the probability that all the clients in g (e.g., clients 1,2 in

(1, 2, 3, 4)) are able to utilize the grants, while all the remainingclients (j∈G′_(b)/g; clients 3 and 4 in our example) are not able to(i.e., j). The size of g represents the eventual transmissions on the RBand hence can be up to M (number of antennas); otherwise, this wouldlead to collisions on all the transmissions on the RB. The addition ofclients to the RB's schedule stops, when no remaining client can furtherincrease the schedule's utility. As the number of clients carefullyscheduled in an RB continues to increase beyond M, it increases thepotential for utilization but it also increases the risk of collisionsfrom overscheduling, thereby resulting in diminishing returns. BETL'sspeculative scheduler strikes a fine balance and typically overschedulesbetween [1, 2M] clients (i.e., f=2) on an RB as determined by Equations(2) and (3).

Importance of Joint Access Distribution: Joint access distribution ofclients is critical for overscheduling. In its absence, one can devise aweighted proportional fair schedule that accounts for the individualaccess probabilities of clients, but will not have the interferencedependency information needed to intelligently overschedule(overscheduling clients sharing common hidden terminals can lead tocollisions or under-utilization). We refer to this as the access-awarescheduler, implemented as follows:

$\begin{matrix}{{E\left( G_{b}^{\prime} \right)} = {\sum_{{{{i \in G_{b}^{\prime}}\&}{G_{b}^{\prime}}}<=M}\frac{(i) \cdot r_{i,b,G_{b}^{\prime}}}{R_{i}}}} & (4)\end{matrix}$

Example: As an example, consider a SISO speculative schedule on an RB.The first client is chosen as

$s_{1} = {{argmax}_{i}{\left\{ {(i) \cdot \frac{r_{i,b}}{R_{i}}} \right\}.}}$The next client to be (over) scheduled on the same RB is chosen asfollows:

$s_{2} = {\arg\;{\max\limits_{i \neq s_{1}}\left\{ {{\left( {i,\overset{\_}{s_{1}}} \right) \cdot \frac{r_{i,b}}{R_{i}}} + {\left( {\overset{\_}{\iota},s_{1}} \right) \cdot \frac{r_{s_{1},b}}{R_{s_{1}}}}} \right\}}}$where

(i, s₁ ) indicates the probability that i is able to transmit, while s₁is not, and vice versa. Note that for SISO,

(i, s₁) and

(ī, s₁ ) don't contribute to useful transmissions, leading to collisionand no-transmission respectively. s₂ is then overscheduled, only if theaccess distributions (interference diversity) of the two clients s₁ ands₂ are such that they allow for a better utilization than the currentschedule, as follows:

$\left\{ {{\left( {s_{2},\overset{\_}{s_{1}}} \right) \cdot \frac{r_{s_{2},b}}{R_{s_{2\;}}}} + {\left( {\overset{\_}{s_{2}},s_{1}} \right) \cdot \frac{r_{s_{1\;,b}}}{R_{s_{1}}}}} \right\} > \left\{ {(i) \cdot \frac{r_{s_{1},b}}{R_{s_{1}}}} \right\}$

Subsequent clients to be overscheduled on the same RB are iterativelyevaluated in a similar procedure using Equations (2) and (3).

A description will now be given regarding scalable measurement overheadin BETL, in accordance with an embodiment of the present invention.

The challenge in executing the proposed scheduler in BETL is the need toestimate the joint access distribution of clients

(g, G_(b)′/g)). For example, one would need to estimate

(1, 2, 3, 4),

(1, 3, 2, 5), and so forth for a M=2 user MU-MIMO speculative schedule.LTE's ability to leverage OFDMA on the UL allows BETL to estimate thejoint access (probability) distributions of clients directly from theirtransmissions, that is, schedule multiple clients jointly in each ULsub-frame and measure their ability to use (transmit on) those scheduledgrants over time. Although data is transferred during these measurementsub-frames, the client schedule is optimized for obtaining the desiredaccess information rather than for performance. Hence, it is imperativeto keep the overhead of this measurement phase as small as possible.

The number of distinct clients (K) that can be scheduled together ineach sub-frame is typically much smaller (less than 10) than the numberof clients in a cell (N). This raises two issues as follows: (i) forlarger MU-MIMO systems, it is not feasible to get any k-client (k∈[1,2M]) joint distribution when k>K, e.g., estimating

(1, 2, 3, 4, 5) (i.e., k=5) is not possible when at most K=4 distinctclients can be scheduled in a sub-frame; and (ii) even when k≤K, if Tsamples (sub-frames) are needed to measure the joint distribution ofeach k-client tuple, then the associated overhead (minimum number ofsub-frames) for estimating all such k-tuples is

$\left\lceil {\frac{\begin{pmatrix}N \\k\end{pmatrix}}{\begin{pmatrix}K \\k\end{pmatrix}}T} \right\rceil$sub-frames, which scales exponentially with k (and hence M) as

$O\;{\frac{N^{m\; i\; n{\{{k,{N - k}}\}}}}{K^{m\; i\; n{\{{k,{K - k}}\}}}}.}$For example, measuring all 6-client joint distributions (for M=3 MUMIMO) in a cell of 20 clients with K=8 requires a minimum of

$\left\lceil {\frac{\begin{pmatrix}20 \\6\end{pmatrix}}{\begin{pmatrix}8 \\6\end{pmatrix}}T} \right\rceil \approx {1384T}$sub-frames.

In contrast, BETL proposed to work with just pair-wise clientdistributions, which in a constant significantly reduced overhead of

$F_{m\; i\; n} = \left\lceil {\frac{\begin{pmatrix}N \\2\end{pmatrix}}{\begin{pmatrix}K \\2\end{pmatrix}}T} \right\rceil$sub-frames (only <7T sub-frames for the above example) that is

$\left. {O\left( \frac{N}{K} \right)}^{2} \right)$and completely independent of M. We determine the schedule of clientsfor successive measurement sub-frames that will estimate all thepair-wise access distributions need in F_(min) sub-frames (lower bound).This being a hard problem in itself, BETL employs the followingscheduling algorithm 1 (in the measurement period) to estimate thesedistributions with as small a number of sub-frames as possible (close toF_(min)).

In each sub-frame during the measurement period, BETL schedules Kclients that will contribute the most value towards measuring pair-wisedistributions; i.e., K clients are chosen, whose resulting pair-wisedistributions have the least number of measurements thus far. Alogarithmic function of the measurement count is employed to ensure thateach pair is sampled for approximately the same number of times at anypoint during the measurement period. This provides for flexibility inusing the measurements even before the end of the period, if desired.

A description will now be given regarding blue-printing interference, inaccordance with an embodiment of the present invention.

Instead of spending the measurement overhead to estimate all the jointaccess distributions, BETL aims to leverage just the pair-wise accessdistribution measurements to “blue-print” the source of the interferenceitself, which in turn is responsible for all the joint client accessdistributions.

The challenge lies in how to blue-print the hidden terminal interferenceon the clients. In other words, given the individual (

(i)=p(i)) and pairwise (

(i,j)=p(i,j)) client access distributions, can we determine the topologycharacterized by (i) the number of hidden terminals (h), (ii) theiraccess distributions (q(k), k∈[1,h]), as well as (iii) their impact onspecific clients (edges, z_(ik), i∈

, k∈[1,h]), that will contribute to these observed distributions. Anedge from a hidden terminal to a client indicates that the latter cansense the former's transmission, when it exists and will defer its own.

FIG. 8 is a block diagram showing an exemplary topology 800 to which thepresent invention can be applied, in accordance with an embodiment ofthe present invention. FIG. 9 is a block diagram showing an exemplarytopology inference objective 900, in accordance with an embodiment ofthe present invention. In FIGS. 8 and 9, a base station (having oroperatively coupled to a processing system 880 for controlling the basestation) is denoted as “eNB” 810, hidden terminals are denoted bycircles (labeled 1 through H), clients are denoted by triangles (labeled1 through N). The topology 800 and objective 800 involve individual ULclient access probabilities denoted by p(i), joint UL client accessprobabilities denoted by p(i,j), and hidden terminal accessprobabilities denoted by q( ).

Similar to wired network topology inference problems, one could employBayesian learning to estimate our wireless interference topology.Specifically, we have applied Monte Carlo Markov Chain (MCMC) basedtechniques, where the interference topology is adapted based onlikelihood estimates such that the topology distribution converges to astationary distribution that maximizes the posterior probability of theobserved data (client access distributions). However, in addition to thetime for convergence, note that the topology only converges indistribution in such an approach. Hence, when the topology informationneeds to be used for real-time scheduling of clients, one needs tosample this distribution to pick an actual topology as mismatches fromthe ground-truth topology could lead to sub-optimality.

While such Bayesian approaches are better suited for large scalenetworks with multiple-hops, the wireless topology that we areinterested in has a single layer of nodes (hidden terminals) and theirinterference edges (to clients) and distributions that need to beestimated. Hence, BETL aims to de sign an alternate deterministicsolution that can leverage this inherent structure to infer the topologywith high accuracy. BETL accomplishes this in two steps.

Step 1: Graph Transformation

A goal of BETL is to infer the topology and access patterns of hiddenterminals that contribute to the observed p(i) and p(i,j) of the clientsin the cell. Let q(k) be the access probability of hidden terminal k.BETL applies a transformation to the access probabilities as follows:

P(i) = −log  (p(i)); Q(k) = −log (1 − q(k))${P\left( {i,j} \right)} = {- {\log\left( \frac{{p(i)} \cdot {p(j)}}{p\left( {i,j} \right)} \right)}}$

The transformation allows us to operate with the sum of the transformedvariables as opposed to the product of the original variables(probabilities). This allows us to now formulate the topology inferenceproblem as a graphical constraint satisfiability problem 1000 as shownin FIG. 10, in accordance with an embodiment of the present invention.The graphical satisfiability problem 1000 involves a first layer ofnodes (shown using squares or rectangles) 1001, a second layer of nodes(shown using circles) 1002, and a third layer of nodes (shown usingtriangles) 1003. The first layer of nodes 1001 and the third layer ofnodes 1003 correspond to each of the input constraints (transformedaccess distributions, P(i) and P(i,j)) that we want to satisfy, whilethe second layer of nodes represents an “unknown” number (h) of hiddenterminals, whose access distributions (Q(k)) and interference impact(edges, z_(ik)) we want to infer. Specifically, we need to determine thetopology (h,Q,Z) that satisfies the following constraints:

$\begin{matrix}{{{{P(i)} = {\sum\limits_{k = 1}^{h}{z_{ik}{Q(k)}}}},{\forall{i \in}}}{{{P\left( {i,j} \right)} = {\sum\limits_{k = 1}^{h}{z_{ik}z_{jk}{Q(k)}}}},{\forall i},{j \in}}} & (5)\end{matrix}$where Z is a matrix, whose entries, Z(i, k)={z_(ik)}, ∀i, k are binaryvariables capturing the impact of hidden terminal k on client i. Thefirst set of constraints captures that the access probability of aclient i is the product of the idle probabilities (1−q_(k)) of allhidden terminals k impacting it (i.e., z_(ik)=1). The second set ofconstraints indicates that the point mass mutual information (P(i,j))between two clients (i,j) is given by the product of the idleprobabilities of all hidden terminals that impact both the clients.Using more variables (hidden terminals, h) than the constraints canresult in an underdetermined system with potentially many solutions.BETL aims to limit the solutions to those that satisfy the aboveconstraints while minimizing the number of hidden terminals (h).

A description will now be given regarding topology inference, inaccordance with an embodiment of the present invention.

BETL infers the topology by starting with an initialized topology(initialization discussed shortly) and then adapts the topology in eachiteration through a gradient approach to improve the satisfiability ofthe constraints. At each iteration, it determines the constraint that ismaximally violated. Then, it selects a hidden terminal {circumflex over(k)}, along with its appropriate topology adaptation (ĥ,{circumflex over(Q)},{circumflex over (Z)}) that will resolve this violation, whileminimizing the violation caused to the other constraints in the process.It terminates when all constraints are satisfied (zero violation), orthe maximum number of iterations is reached, in which case theconfiguration with the least aggregate violation is chosen.

Topology Adaptation: There are multiple cases to consider during theadaptation process in each iteration.

Case 1: If the constraint chosen for restoring violation is anindividual access constraint, P(i), two sub-cases arise based on thetype of violation. Let c_(i)=Σ_(k=1) ^(h)z_(ik)Q(k)−P(i).

(i) Over-contribution (c_(i)>0): BETL reduces the contribution bydetermining whether to decrease the appropriate contribution({circumflex over (Q)}(k)←Q (k)−c_(i)); (or) remove an edge completely({circumflex over (z)}_(ik)=0) from one of the existing hidden terminalsk (impacting client i), where k:z_(ik)=1.

(ii) Under-contribution (c_(i)<0): BETL determines whether to increasethe appropriate contribution ({circumflex over (Q)}(k)←Q(k)+|c_(i)|)from one of its hidden terminals k; (or) add an edge to one of theexisting hidden terminals k (where z_(ik)=0) to avail its contribution(Q(k)) to P(i); (or) add a new hidden terminal k′ with an edge to it({circumflex over (z)}_(ik′)=1) that provides the missing contribution({circumflex over (Q)}(k′)=|c_(i)|).

Case 2: Similarly, if the constraint chosen is a joint accessconstraint, P(i,j), the corresponding scenarios are slightly moreinvolved. Let c_(i,j)=Σ_(k=1) ^(h)z_(ik)z_(jk)Q(k)−P(i,j).

(i) Over-contribution (c_(i,j)>0): BETL determines whether to reduce theappropriate contribution ({circumflex over (Q)}(k)←Q (k)−c_(i,j)) fromone of the contributing hidden terminals, k:z_(ik)z_(jk)=1; (or) removean edge from one or both of the clients ({circumflex over (z)}_(ik)=0and/or {circumflex over (z)}_(jk)=0) impacted by that hidden terminal.

(ii) Under-contribution (c_(i,j)>0): BETL determines whether to increasethe appropriate contribution ({circumflex over (Q)}(k)←Q(k)+|c_(i,j)|)from one of its contributing hidden terminals k: z_(ik)z_(jk)=1; (or)add edge(s) to a hidden terminal k to avail its contribution (Q(k)),where an edge to only one or neither clients (i and j) exists, i.e.,k:z_(ik)+z_(ik)≤1; (or) add a new hidden terminal (k′) with two edges,one each to i and j ({circumflex over (z)}_(ik′)=1, {circumflex over(z)}_(jk′)=1) that provides the missing contribution ({circumflex over(Q)}(k′)=|c_(i,j)|) to P(i,j).

At the end of the adaptation, hidden terminals left with no edges toclients are removed and the resulting topology (ĥ,{circumflex over(Q)},{circumflex over (Z)}) serves as input (h,Q,Z) to the nextiteration.

A description will now be given regarding topology initialization, inaccordance with an embodiment of the present invention.

Given the non-linear nature of the problem, a gradient based approach isnot guaranteed to converge to an optimal solution and could end up in alocally optimal topology. To alleviate the resulting sub-optimality aswell as to minimize the number of hidden terminals employed, BETL runsthe inference algorithm by initializing with different startingtopologies and picking the inferred topology with the least number ofhidden terminals that yields the smallest violation. In addition tostarting with random topologies with varied number of hidden terminals,it also picks from those that satisfy only one set of the constraints asstarting topologies. Given the single layer of variables that need to beinferred, such a multi-point initialization is able to overcome localoptima in most cases, thereby enabling the deterministic algorithm inBETL to yield high accuracies in topology inference.

A description will now be given of some additional considerationrelating to blue-printing interference, in accordance with an embodimentof the present invention.

(1) Occasionally, when the number of hidden terminals is much largerthan clients, multiple topologies (solutions) may satisfy the observedpair-wise client access distributions, making it infeasible to pinpointthe ground-truth topology. However, even in such cases, there is a largesimilarity between the topology inferred by BETL and ground-truth, whichleads to a minimal degradation in BETL's scheduler performance. Further,in such scenarios, additional joint access distribution of clients(beyond pair-wise, say triplets) that maybe available (obtained) fromexisting (new) measurements, can provide additional constraints, whichwill significantly reduce the number of feasible topologies.

(2) BETL's topology inference currently assumes that the interferenceimpact of a hidden terminal on different clients has a binary {0,1}effect. While this will capture scenarios where clients are eitherstrongly or weakly interfered by the hidden terminal, it does notaccurately capture the fractional [0,1] impact resulting from fadingrelated interference variations. However, it must be noted that thesub-optimality resulting from this assumption is restricted to thespecific clients in question. Hence, this does not appreciably affectthe benefits to speculative scheduling, especially in the presence of areasonable number of clients in the cell.

A description will now be given regarding generating higher-orderdistributions, in accordance with an embodiment of the presentinvention.

Having inferred the blue-print of the interference topology

={h*,Q*,Z*}, we now demonstrate how BETL can compute the higher orderaccess distributions from just the individual client accessdistributions,

(u_(i)).

Recall from Equation (3), we need to compute

(g, G_(b)′/g) i.e., the probability that all the clients in g are ableto utilize the grants, while all the remaining clients (in G_(b)′/g) arenot able to. Without loss of generality, let us assume the following:U _(n) ={u ₁ ,u ₂ , . . . ,u _(n) };V _(m) ={v ₁ ,v ₂ , . . . ,v _(m)}g=U _(n) ;G _(b) ′=U _(n) ∪V _(m)

Hence, we are interested in computing

(U_(n), V_(m) ) Applying Baye's theorem, we have the following:

(U _(n), V _(m) )=

( V _(m) |U _(n))·

(U _(n))  (6)

With the help of the inferred topology

, we can now compute

(U_(n)) and

(V_(m) |U_(n)) easily.

P(U_(n)) can be further simplified as follows:

$\begin{matrix}{{\left( {u_{1},\ldots\mspace{14mu},u_{n}} \right)} = {{\left( \left( {u_{1},\ldots\mspace{14mu},u_{n - 1}} \right) \middle| u_{n} \right) \cdot}\left( u_{n} \right)}} \\{= {{\left( U_{n - 1} \middle| u_{n} \right) \cdot}\left( u_{n} \right)}}\end{matrix}$

Computing

(U_(n-1)|u_(n)) on

is equivalent to computing just

(U_(n-1)) but on a modified topology that is conditioned given theoccurrence of u_(n) as shown in FIG. 11. That is, FIG. 11 is a blockdiagram showing an exemplary topology conditioning 1100, in accordancewith an embodiment of the present invention. Given u_(n)'s occurrence,the topology gets updated (conditioned) by removing the hidden terminals{circumflex over (k)} (denoted by the reference numerals R1 and R2) thathave an edge to u_(n) (i.e., z_(u) _(n) _(,{circumflex over (k)})=1) andthe access probabilities on this conditioned topology (

|u_(n)) are updated using Equation (5) and represented as

_(u) _(n) (⋅), where

u n ⁢ ( u i ) = ⁢ ( u i ) Π k ^ : ( z u i , k ^ = 1 ) ⁡ ( 1 - q ⁡ ( k ^ ) )

Thus, Equation (7) can be computed by recursively conditioning thetopology (

|u_(n), u_(n-1), . . . ) on the occurrence of each client in U_(n) untilit includes just the individual client access probabilities as follows:

⁢( U n ) = ⁢ ⁢ ( u n ) · u n ⁢ ( u n - 1 ) · u n , u n - 1 ⁢ ( u n - 2 ) ⁢ ⁢ …= ⁢ ⁢ ( u n ) · ( ∏ a = 1 n - 1 ⁢ u n , … ⁢ , u n - a + 1 ⁢ ( u n - a ) ) ⁢ (7 )

Focusing on the other term,

(V_(m) |U_(n)) in Equation (6), this is essentially

(V_(m) ) on the topology conditioned by the occurrence of all theclients in U_(n), i.e.,

(V_(m) |U_(n)))=

_(U) _(n) (V_(m) ). Applying Baye's theorem, we have the following:

U n ⁢ ( V m _ ) = ⁢ ( 1 - U n ⁢ ( V m - 1 _ | v m ) · U n ⁢ ⁢ ( v m ) U n ⁢ (V m - 1 ) ) · U n ⁢ ( V m - 1 _ ) = ⁢ ( 1 - U n , v m ⁢ ( V m - 1 _ ) · U n⁢⁢( v m ) U n ⁢ ( V m - 1 ) ) · U n ⁢ ( V m - 1 _ ) ( 8 )

As before, the above equation can be simplified by recursivelyconditioning the topology on the various clients in V_(m) until itconsists of just the individual access probabilities of clients in V_(m)at various stages of the topology conditioning. Using Equations (7) and(8) in Equation (6), we are now able to compute the required higherorder access distributions from just the individual client accessdistributions on the various conditioned topologies.

An example will now be described. In a four client (2 user MU-MIMO)schedule grant, the joint access distribution of clients 3 and 4 beingable to transmit, while 1 and 2 not being able to, can be computed usingthe source interference topology and its conditioned versions asfollows:

$\mspace{20mu}{{\left( {\overset{\_}{1},\overset{\_}{2},3,4} \right)} = {{\left( \left( {\overset{\_}{1},\overset{\_}{2}} \right) \middle| \left( {3,4} \right) \right) \cdot}\left( {3,4} \right)}}$⁢where ⁢ ⁢ ⁢ ( 3 , 4 ) = ⁢ ( 3 | 4 ) · ⁢ ( 4 ) = 4 ⁢ ( 3 ) · ⁢ ( 4 ) ⁢ ( ( 1 _ ,2 _ ) | ( 3 , 4 ) ) = 3 , 4 ⁢ ( 1 _ , 2 _ ) = ( 1 - 3 , 4 , 2 ⁢ ( 1 _ ) ·3 , 4 ⁢ ( 2 ) 3 , 4 ⁢ ( 1 _ ) ) · 3 , 4 ⁢ ( 1 _ ) .

Embodiments described herein may be entirely hardware, entirely softwareor including both hardware and software elements. In a preferredembodiment, the present invention is implemented in software, whichincludes but is not limited to firmware, resident software, microcode,etc.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable medium such as a semiconductor or solid state memory,magnetic tape, a removable computer diskette, a random access memory(RAM), a read-only memory (ROM), a rigid magnetic disk and an opticaldisk, etc.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope and spirit of the invention as outlined by the appendedclaims. Having thus described aspects of the invention, with the detailsand particularity required by the patent laws, what is claimed anddesired protected by Letters Patent is set forth in the appended claims.

What is claimed is:
 1. A system for blue-printing interference formobile access in an unlicensed spectrum of a synchronous scheduledcellular access system, the system comprising: a cellular base stationhaving a processor, configured to construct and execute an intelligentmeasurement schedule of clients for uplink transmissions to obtainaccess measurements for the uplink transmissions, the intelligentmeasurement schedule being constructed for scalable access measurementoverhead, the access measurements indicating interference dependenciesbetween the clients; estimate an interference topology and statistics ofthe interference topology, from the access measurements to form aninterference blueprint; adjust the intelligent measurement schedule tooverschedule the clients for the uplink transmissions to reduce spectrumutilization loss while minimizing client transmission collisions, basedon the interference blueprint; and initiate the uplink transmissions forthe clients in accordance with the adjusted intelligent measurementschedule.
 2. The system of claim 1, wherein the intelligent measurementschedule is constructed with a preference for obtaining the accessinformation versus uplink performance.
 3. The system of claim 1, whereinthe intelligent measurement schedule is constructed to minimize theaccess measurement overhead.
 4. The system of claim 1, wherein theprocessor constructs the intelligent measurement schedule to jointlyschedule pairs of the clients and execute the intelligent measurementschedule to obtain the access measurements and from which to inferindividual and pair-wise access probabilities of the clients.
 5. Thesystem of claim 1, wherein the interference topology and the statisticsof the inference topology are estimated using a graphical constraintsatisfiability problem solvable by a transformation applied to accessprobabilities of individual ones and pairs of the clients.
 6. The systemof claim 5, wherein a number of hidden interfering terminals, accessprobabilities of the hidden interfering terminals, and interferencedependencies between the hidden interfering terminals are determined bysolving the graphical constraint satisfiability problem.
 7. The systemof claim 5, wherein the graphical constraint satisfiability probleminvolves a graph having (i) a first set of vertices representing theclients and having a value that is a function of the accessprobabilities of the individual ones of the clients, (ii) a second setof vertices representing a set of unknown number of hidden interferingterminals and respective unknown access probabilities for the set ofunknown number of hidden interfering terminals, and (iii) a third set ofvertices representing pairs of clients with a value that is a functionof joint access probabilities for the pairs of the clients.
 8. Thesystem of claim 5, wherein the graphical constraint satisfiabilityproblem is solved by using an iterative process that comprises theprocessor (i) initializing a starting inference topology randomly fromamong a set of interfering topologies, (ii) at every iteration, adoptinga gradient approach that picks a constraint, from a set of constraints,that yields a maximum violation, and selecting a topology adaptationthat resolves the maximum violation while minimizing other respectiveviolations caused by other ones of the constraints in the set, and (iii)select a respective one of the interfering topologies from the sethaving a violation below a threshold amount.
 9. The system of claim 1,wherein the intelligent measurement schedule is constructed by addingclients, one at a time, to the intelligent measurement schedule astransmission slots such that each of the added clients yields a maximumvalue for measurement statistics based on a current state of theintelligent measurement schedule.
 10. The system of claim 1, wherein theinterference topology and the statistics of the interference topologyrelate to a set of hidden terminals.
 11. A computer-implemented methodfor blue-printing interference for mobile access in an unlicensedspectrum of a synchronous scheduled cellular access system, the methodcomprising: constructing and executing, by a processor of a cellularbase station, an intelligent measurement schedule of clients for uplinktransmissions to obtain access measurements for the uplinktransmissions, the intelligent measurement schedule being constructedfor scalable access measurement overhead, the access measurementsindicating interference dependencies between the clients; estimating, bythe processor, an interference topology and statistics of theinterference topology, from the access measurements to form aninterference blueprint; adjusting, by the processor, the intelligentmeasurement schedule to overschedule the clients for the uplinktransmissions to reduce spectrum utilization loss while minimizingclient transmission collisions, based on the interference blueprint; andinitiating, by the processor, the uplink transmissions for the clientsin accordance with the adjusted intelligent measurement schedule. 12.The computer-implemented method of claim 11, wherein the intelligentmeasurement schedule is constructed with a preference for obtaining theaccess information versus uplink performance.
 13. Thecomputer-implemented method of claim 11, wherein the intelligentmeasurement schedule is constructed to minimize the access measurementoverhead.
 14. The computer-implemented method of claim 11, wherein theprocessor constructs the intelligent measurement schedule to jointlyschedule pairs of the clients and execute the intelligent measurementschedule to obtain the access measurements from which to inferindividual and pair-wise access probabilities of the clients.
 15. Thecomputer-implemented method of claim 11, wherein the interferencetopology and the statistics of the inference topology are estimatedusing a graphical constraint satisfiability problem solvable by atransformation applied to access probabilities of individual ones andpairs of the clients.
 16. The computer-implemented method of claim 15,wherein a number of hidden interfering terminals, access probabilitiesof the hidden interfering terminals, and interference dependenciesbetween the hidden interfering terminals are determined by solving thegraphical constraint satisfiability problem.
 17. Thecomputer-implemented method of claim 15, wherein the graphicalconstraint satisfiability problem involves a graph having (i) a firstset of vertices representing the clients and having a value that is afunction of the access probabilities of the individual ones of theclients, (ii) a second set of vertices representing a set of unknownnumber of hidden interfering terminals and respective unknown accessprobabilities for the set of unknown number of hidden interferingterminals, and (iii) a third set of vertices representing pairs ofclients with a value that is a function of joint access probabilitiesfor the pairs of the clients.
 18. The computer-implemented method ofclaim 15, wherein the graphical constraint satisfiability problem issolved by using an iterative process that comprises the processor (i)initializing a starting inference topology randomly from among a set ofinterfering topologies, (ii) at every iteration, adopting a gradientapproach that picks a constraint, from a set of constraints, that yieldsa maximum violation, and selecting a topology adaptation that resolvesthe maximum violation while minimizing other respective violationscaused by other ones of the constraints in the set, and (iii) select arespective one of the interfering topologies from the set having aviolation below a threshold amount.
 19. The computer-implemented methodof claim 11, wherein the intelligent measurement schedule is constructedby adding clients, one at a time, to the intelligent m measurementschedule as transmission slots such that each of the added clientsyields a maximum value for measurement statistics based on a currentstate of the intelligent measurement schedule.
 20. A computer programproduct for blue-printing interference for mobile access in anunlicensed spectrum of a synchronous scheduled cellular access system,the computer program product comprising a non-transitory computerreadable storage medium having program instructions embodied therewith,the program instructions executable by a computer of a cellular basestation to cause the computer to perform a method comprising:constructing and executing, by a processor of the computer, anintelligent measurement schedule of clients for uplink transmissions toobtain access measurements for the uplink transmissions, the intelligentmeasurement schedule being constructed for scalable access measurementoverhead, the access measurements indicating interference dependenciesbetween the clients; estimating, by the processor, an interferencetopology and statistics of the interference topology, from the accessmeasurements to form an interference blueprint; adjusting, by theprocessor, the intelligent measurement schedule to overschedule theclients for the uplink transmissions to reduce spectrum utilization losswhile minimizing client transmission collisions, based on theinterference blueprint; and initiating, by the processor, the uplinktransmissions for the clients in accordance with the adjustedintelligent measurement schedule.